Now that PEP 572 is done, I don't ever want to have to fight so hard for a PEP and find that so many people despise my decisions. I would like to remove myself entirely from the decision process. I'll
still be there for a while as an ordinary core dev, and I'll still be
available to mentor people -- possibly more available. But I'm basically
giving myself a permanent vacation from being BDFL, and you all will be on your own.

When studying Probability & Statistics, one of the first and most important theorems students learn is the Bayes' Theorem. This theorem is the foundation of deductive reasoning, which focuses on determining the probability of an event occurring based on prior knowledge of conditions that might be related to the event. The Naive Bayes Classifier brings the power of this theorem to Machine Learning, building a very simple yet powerful classifier. In this article, we will see an overview on how this classifier works, which suitable applications it has, and how to use it in just a few lines of Python and the Scikit-Learn library.

Today we’re going to explore a wonderful feature that the Python library offers to you out of the box: the serialization. To serialize an object means to transform it in a format that can be stored, so as to be able to deserialize it later, recreating the original object from the serialized format. To do all these operations we will use the pickle module.

A multi-core Python HTTP server that is about 40% to 110% faster than Go can be built by relying on the Cython language and LWAN C library. A proof of concept validates the possibility of high performance system programming in the Cython language.

We’ll design and implement a toy spam detection pipeline to demonstrate how to leverage streaming analytics to tackle the issue. We’ll also sketch out the next steps needed to move this solution into production.

I’ve been working lately in a command line application called Bard which is a music manager for your local music collection. Bard does an acoustic fingerprinting of your songs (using acoustid) and stores all song metadata in a sqlite database. With this, you can do queries and find song duplicates easily even if the songs are not correctly tagged. I’ll talk in another post more about Bard and its features, but here I wanted to talk about the algorithm to find song duplicates and how I optimized it to run around 8000 times faster.

But there always be questions for me and my friends: “How can we tell, at a single glance, whether the jet is Boeing or Airbus”. Although experts and enthusiasts can easily distinguish different jets, my friends and I often have hard time correctly identify them.